What you would learn in Fundamentals of Deep Learning: Core Concepts and PyTorch course?
Are you interested in Artificial Intelligence (AI), Machine Learning, and Artificial Neural networks?
Are you scared of getting involved with Deep Learning because it sounds too complicated?
You've been looking at Deep Learning videos but still don't "get" it?
I've been there! I don't have a formal engineering education, and I learned how to code by myself. However, AI seemed to be impossible to achieve.
This course was created to help you avoid months of frustration and figure out Deep Learning. After completing the course, you'll be prepared to tackle other advanced and cutting-edge subjects in AI.
The course teaches:
We will assume that you have as little knowledge of the subject as possible. No computer science or engineering knowledge is required (except for the basic Python understanding). Are you not familiar with all the maths required to run Deep Learning? It's okay, and we'll cover each step-by-step.
It will "reinvent" deep neural networks to give you a deep understanding of its fundamental mechanisms. This will help you become more at ease working with Deep Learning and understand the topic.
We'll also create a primary neural network entirely from scratch using PyTorch along with PyTorch Lightning and then train an MNIST model to train handwritten recognition of digits.
After you have completed the course
It will be apparent that you have the "intuitive" understanding of Deep Learning and feel confident that you have a good grasp of the subject.
If you return to the famous courses you've had difficulty understanding before (like Andrew Ng's courses or Jeremy Howard's Fastai class), you'll be amazed by how much you'll understand.
It will be able to comprehend what experts such as Geoffrey Hinton are writing about in their articles or what Andrej Karpathy is talking about on Tesla Autonomy Day.
The students will be proficient with the theoretical and practical understanding to explore the more modern neural network designs such as Convolutional Neural networks (CNN), Recurrent Neural networks (RNN), and transformers. And begin your journey towards the very cutting-edge of AI Supervised, Unsupervised and Supervised learning, and many more.
It is possible to begin experimenting using your AI-related projects by using PyTorch as well as Supervised Learning
Content of the Course:
Learn to be able to comprehend Deep Learning intuitively
A clear and intuitive understanding of fundamental math concepts that underlie Deep Learning
A detailed look at how deep neural networks function under the hood
The Computational graph (on which libraries such as PyTorch as well as Tensorflow are based)
Create neural networks from scratch with PyTorch as well as PyTorch Lightening
You'll be able to explore the latest advances in AI and other advanced neural networks such as CNNs, RNNs, and Transformers.
You'll understand the deep learning concepts that experts are discussing in interviews and articles.
You'll be able to begin exploring your own AI projects with PyTorch
Download Fundamentals of Deep Learning: Core Concepts and PyTorch from below links NOW!